library("syuzhet")
## Warning: package 'syuzhet' was built under R version 4.2.2
library("wordcloud")
## Warning: package 'wordcloud' was built under R version 4.2.2
## Loading required package: RColorBrewer
library("plotly")
## Warning: package 'plotly' was built under R version 4.2.2
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.2.2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library("tm")
## Warning: package 'tm' was built under R version 4.2.2
## Loading required package: NLP
## 
## Attaching package: 'NLP'
## The following object is masked from 'package:ggplot2':
## 
##     annotate
library("tidytext")
## Warning: package 'tidytext' was built under R version 4.2.2
library("dplyr")
## Warning: package 'dplyr' was built under R version 4.2.2
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library("RColorBrewer")
library("ggplot2")
library("magrittr")
library("stringr")
## Warning: package 'stringr' was built under R version 4.2.2
library("twitteR")
## Warning: package 'twitteR' was built under R version 4.2.2
## 
## Attaching package: 'twitteR'
## The following objects are masked from 'package:dplyr':
## 
##     id, location
CONSUMER_SECRET <- "c4lW91jDaRUgzFAvOr0YleNYVEm17ZTnEHnMsvlDcjKSJnBJXg"
CONSUMER_KEY <- "pgjt5UWX2uzuK6qRwHowEPPXq"
ACCESS_SECRET <- "txachtOgZ7L0dyj9vAswZkfGjtJMfU4fVvRdAyEo3jNg0"
ACCESS_TOKEN <- "1594899110103986176-QoH3VZuNGyLd8RBuY6cnFR4lN6obA4"

setup_twitter_oauth(consumer_key = CONSUMER_KEY,
                    consumer_secret = CONSUMER_SECRET,
                    access_token = ACCESS_TOKEN,
                    access_secret = ACCESS_SECRET)
## [1] "Using direct authentication"
trendTweets <- searchTwitter("#China -filter:retweets", n=10000, lang="en", since="2022-11-26", until="2022-12-2",
                             retryOnRateLimit = 120)

trendTweetsDF <- twListToDF(trendTweets)
class(trendTweetsDF)
## [1] "data.frame"
names(trendTweetsDF)
##  [1] "text"          "favorited"     "favoriteCount" "replyToSN"    
##  [5] "created"       "truncated"     "replyToSID"    "id"           
##  [9] "replyToUID"    "statusSource"  "screenName"    "retweetCount" 
## [13] "isRetweet"     "retweeted"     "longitude"     "latitude"
View(trendTweetsDF)
head(trendTweetsDF)[1:5]
head(trendTweetsDF$text)[1:5]
## [1] "#Bitcoin #USA #King #International Bitcoin\n#Congo #England #Kenya #China #Siam #Portugal El Salvador #Italy #Brazil… https://t.co/9OCnrHl47V"  
## [2] "@KINGDADADA4 @realdogen @GreenXmasERC #RabbitInu probably the best meme coin 2023 #RINU #China https://t.co/KKeBjneAw3"                         
## [3] "Deputy Minister Tsai gave an interview to @TVPWorld_com’s @FilipStyczynski. He detailed #Taiwan’s🇹🇼 strengthening o… https://t.co/jhKXeORp3y"   
## [4] "Reasonable analysis of current Events in #China.\n\n#ChinaProtests https://t.co/QSDDNoafD1"                                                     
## [5] "A video of an audio statement in Arabic titled:\nLet Whoever Hates Me Dig Their Own Grave.\n19- Rabi Than-1444AH \n13-… https://t.co/b5gxKc8cZV"
save(trendTweetsDF,file = "trendingTweetsDF.Rdata")

load(file = "trendingTweetsDF.Rdata")

tweetsDF1 <- trendTweetsDF %>%
  select(screenName, text, created, statusSource)
save(tweetsDF1, file = "tweetsDF.Rdata")
#Checking missing values
nien <- sapply(trendTweetsDF, function(x) sum(is.na(x)))
nien
##          text     favorited favoriteCount     replyToSN       created 
##             0             0             0          8624             0 
##     truncated    replyToSID            id    replyToUID  statusSource 
##             0          8782             0          8624             0 
##    screenName  retweetCount     isRetweet     retweeted     longitude 
##             0             0             0             0          9956 
##      latitude 
##          9956

#Subsetting dyplr

tweetsDF0 <- trendTweetsDF %>%
  select(screenName,text,created,statusSource)
tweetsDF0

#Assembling the data created

tweetsDF0 %>%  
  group_by(1) %>%  
  summarise(max = max(created), min = min(created))
reviour <- tweetsDF1 %>%  mutate(Created_At_Round = created %>% round(units = 'hours') %>% as.POSIXct())
reviour
tweetsDF0 %>% pull(created) %>% min() 
## [1] "2022-11-30 04:54:57 UTC"
tweetsDF0 %>% pull(created) %>% max()
## [1] "2022-12-01 23:59:57 UTC"

#Arranging the plot usuing plotly and ggplot

ggplot(data = tweetsDF1, aes(x = created)) +
  geom_histogram(aes(fill = ..count..)) +
  theme(legend.position = "upper right") +
  xlab("Time") + ylab("Number of tweets") + 
  scale_fill_gradient(low = "grey",high = "blue")
## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#Plotting

Sourceplotss <- function(x) {
  if(grepl(">Twitter for iPhone</a>", x)){
    "iphone"
  }else if(grepl(">Twitter for iPad</a>", x)){
    "ipad"
  }else if(grepl(">Twitter for Android</a>", x)){
    "android"
  } else if(grepl(">Twitter Web Client</a>", x)){
    "Web"
  } else if(grepl(">Twitter for Windows Phone</a>", x)){
    "windows phone"
  }else if(grepl(">dlvr.it</a>", x)){
    "dlvr.it"
  }else if(grepl(">IFTTT</a>", x)){
    "ifttt"
  }else if(grepl(">Facebook</a>", x)){  
    "facebook"
  }else {
    "others"
  }
}


tweetsDF0$tweetSource = sapply(tweetsDF0$statusSource,Sourceplotss)

tweetplotts <- tweetsDF0 %>% 
  select(tweetSource) %>%
  group_by(tweetSource) %>%
  summarize(count=n()) %>%
  arrange(desc(count)) 

ggplot(tweetsDF0[tweetsDF0$tweetSource != 'others',], aes(tweetSource, fill = tweetSource)) +
  geom_bar() +
  theme(legend.position="none",
        axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  ylab("Number of tweets") +
  ggtitle("Tweets by Source")

#Accounts that Tweets about China

tweetplotss <- tweetsDF0 %>%
  select(screenName) %>%
  group_by(screenName) %>%
  summarize(count=n()) %>%
  arrange(desc(count)) 

#CORPUS

corpse <- Corpus(VectorSource(tweetsDF1$screenName))  
class(tweetsDF1$screenName)
## [1] "character"
oiu <- class(VectorSource(tweetsDF1$screenName))
oiu
## [1] "VectorSource" "SimpleSource" "Source"
str(corpse)
## Classes 'SimpleCorpus', 'Corpus'  hidden list of 3
##  $ content: chr [1:10000] "JamesWe42087922" "PigeArmy888" "MOFA_Taiwan" "neferimhotep" ...
##  $ meta   :List of 1
##   ..$ language: chr "en"
##   ..- attr(*, "class")= chr "CorpusMeta"
##  $ dmeta  :'data.frame': 10000 obs. of  0 variables
class(corpse)
## [1] "SimpleCorpus" "Corpus"
kin <- corpse
kin
## <<SimpleCorpus>>
## Metadata:  corpus specific: 1, document level (indexed): 0
## Content:  documents: 10000

WORDCLOUD FOR SCREEN_NAMES.

cloud <- brewer.pal(8, "Dark2")
clouds <- cloud[-(1:4)]
set.seed(123)

par(mar = c(0,0,0,0), mfrow = c(1,1))

wordcloud(words = corpse, scale=c(3,0.5),
          max.words=500,
          random.order=FALSE,
          rot.per=0.5,
          use.r.layout=TRUE,
          colors=cloud)
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